Cloud-native platforms have transformed modern software delivery by enabling scalable, resilient, and continuously evolving digital services. Organizations increasingly rely on microservices architectures, containerized applications, continuous integration and continuous deployment pipelines, and cloud-based infrastructure to deliver digital products rapidly and efficiently. However, the complexity of cloud-native environments introduces significant challenges relating to software quality, service reliability, user experience consistency, and operational resilience. Traditional quality assurance approaches often struggle to provide timely feedback within highly dynamic delivery ecosystems, resulting in increased defect leakage, service instability, customer dissatisfaction, and operational inefficiencies. Consequently, organizations are adopting intelligent quality engineering approaches that combine product analytics, automated testing, and user feedback mechanisms to improve software quality and digital service reliability. This study investigates the effectiveness of integrating these capabilities within cloud-native platforms. A quantitative research design was employed using operational data collected from twelve cloud-native software organizations over a twelve-month period. Key performance indicators including service reliability rate, defect escape rate, user satisfaction score, incident frequency index, and quality engineering effectiveness score were analyzed. The findings indicate that organizations implementing intelligent quality engineering frameworks achieved significantly higher service reliability, reduced production defects, improved customer satisfaction, lower incident occurrence, and stronger operational performance compared with organizations utilizing conventional quality assurance practices. Statistical analysis revealed a strong positive relationship between quality engineering maturity and digital service reliability. The study proposes an integrated framework for combining product analytics, automated testing, and user feedback to support continuous quality improvement within cloud-native ecosystems. The findings contribute to software engineering knowledge and provide practical guidance for organizations seeking to improve digital service reliability in increasingly complex cloud-native environments.